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1 Epidemiology - Lecture #10 CHP 646 Dr. Holly Gaff.

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Presentation on theme: "1 Epidemiology - Lecture #10 CHP 646 Dr. Holly Gaff."— Presentation transcript:

1 1 Epidemiology - Lecture #10 CHP 646 Dr. Holly Gaff

2 2 Lecture Overview Gordis - Chapter 14 –Inference Gordis Chapter 15 –Bias, confounding and interaction Gordis - Chapter 16 –Genetic and environmental factors Brief overview of adjusting RR and OR

3 3 Etiology of disease Study of causation of diseases Does an observed association reflect a causal relationship?

4 4 Approaches Animal models In vitro studies Observations in human populations

5 5 Human populations 1.Clinical observations 2.Identify and analyze available data 3.New studies –Case-control study Identify suspect exposures –Cohort study Follow up to see if associations hold –Randomized trials Usually only for beneficial agents

6 6 Two-step process 1.Identify an association between exposure or characteristic and risk of disease using both ecological, case- control and cohort studies 2.Determine if association is likely to be causal

7 7 Ecological Studies Population level studies No linking of individuals and their specific exposure to their specific disease risk No accounting for variation Ecological fallacy

8 8 Example Oikos, ahead of print Tomas Grim, A possible role of social activity to explain differences in publication output among ecologists

9 9 Ecological studies So are they any good? Can shed light on previously unexplored areas Useful as long as you remember they dont show causation!

10 10 Interpreting Associations

11 11 Interpreting Associations Very hard Very controversial Very, very difficult to tease apart and identify root cause rather than confounding (noncausal) factors Example: low-birth rate among female smokers

12 12 Types of causal relationships Necessary and sufficient Necessary, but not sufficient Sufficient, but not necessary Neither sufficient nor necessary

13 13 Necessary and Sufficient Without factor, a disease will never develop (necessary) With factor, a disease will always develop (sufficient) Examples: rarely occurs

14 14 Necessary, but not Sufficient Without factor, a disease will never develop (necessary) With factor, a disease will not develop (not sufficient) - other factors are required Examples: most infectious diseases

15 15 Sufficient, but not Necessary Without factor, a disease may or may not develop (not necessary) With factor, a disease will always develop (sufficient) Examples: maybe some radiation related cancers

16 16 Neither Sufficient nor Necessary Without factor, a disease may or may not develop (not necessary) With factor, a disease may or may not develop (not sufficient) Examples: most chronic diseases

17 17 Guidelines for causality 1.Temporal relationship Exposure BEFORE disease Disease occurrence logical with standard progression, e.g., after latent period Easiest with prospective cohort studies

18 18 Guidelines for causality 2.Strength of association Measured by relative risk and/or odds ratio 3.Dose-response relationship As exposure increases, risk of disease increases

19 19 Guidelines for causality 4.Replication of findings Consistent across different studies with different populations Generalizability 5.Biologic plausibility Seek consistency of epidemiological findings with known biology Sometimes limits advances!!

20 20 Guidelines for causality 6.Consideration of alternative explanations Rule out other possible alternatives 7.Cessation of exposure If exposure stops, does risk decrease? Not always possible if process if irreversible

21 21 Guidelines for causality 8.Consistency with other knowledge Sales data Media information 9.Specificity of the association Exposure is linked with only one disease Absence does not negate causal relationship

22 22 Example of causality MMR vaccination and autism Is there a relationship? Certainly hyped in media and cause for great controversy Lets walk through the guidelines…

23 23 MMR and autism 1.Temporal relationship symptoms of autism had set in within days of vaccination at approximately 14 months Average age of diagnosis of autism is 3.1 years of age MMR given around 13 months of age

24 24 2.Strength of association N Engl J Med Nov 7;347(19):

25 25 MMR and autism 3.Dose-response relationship JAMA. 2001;285:

26 26 MMR and autism 4.Replication of findings H Honda et al. No effect of MMR withdrawal on the incidence of autism: a total population study. Journal of Child Psychology and Psychiatry 2005 doi: /j x

27 27 MMR and autism 5.Biologic plausibility: Dr. Wakefield proposed the following sequence of events following the MMR vaccinations: 1.MMR vaccination 2.Chronic measles infection 3.Immune-mediated vasculitis 4.Focal ischemia and intestinal inflammation with ulceration of the overlying epithelium 5.Gastrointestinal symptoms and macroscopic features of the bowel which mimic Crohns disease 6.Increased permeability of the gut wall to exogenous peptides 7.Circulating toxic peptides interfere with neuroregulation and brain development 8.Development of clinical autism

28 28 MMR and autism 6.Consideration of alternative explanations Genetics? Toxic substances?

29 29 MMR and autism 7.Cessation of exposure H Honda et al. No effect of MMR withdrawal on the incidence of autism: a total population study. Journal of Child Psychology and Psychiatry 2005 doi: /j x

30 30 MMR and autism 8.Consistency with other knowledge Little is known about autism 9.Specificity of association Clearly not only linked with autism

31 31 MMR and autism So final conclusion??? In a 2001 investigation by the Institute of Medicine, a committee concluded that the "evidence favors rejection of a causal relationship.... between MMR vaccines and autistic spectrum disorders (ASD)." The committee acknowledged, however, that "they could not rule out" the possibility that the MMR vaccine could contribute to ASD in a small number of children. While other researchers agree the data does not support a link between the MMR and autism, more research is clearly needed.

32 32 Criteria lists abound Everyone likes to come up with their own list of things to check, but major factors are: –Temporal relationship –Biological plausibility –Consistency –Confounding and alternative explanations explored

33 33 Discussion Brain cancer and cell phone use? Smoking and lung cancer? Others?

34 34 Causal guidelines 1.Temporal relationship 2.Strength of association 3.Dose-response relationship 4.Replication of findings 5.Biologic plausiblity 6.Alternative explanations 7.Cessation of exposure 8.Consistency with other knowledge 9.Specificity of the association


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